

mvpsmod_fixtheta <- function(formula, data, trt, coords, nsamp, nburn, thin,
		tuning = list(A = 0.1, psi = 0.2, theta = 1), 
		prior = list(KIG = rep(.01,2), psi = rep(.01,2), theta1 = rep(.6,2), theta2 = rep(10,2)),
		starting = list(B=NULL, A = NULL, psi = NULL, theta = NULL)){

	require(spBayes)
	require(corpcor)
	require(MASS)

	ncov <- dim(model.matrix(formula, data))[2]


	print("Note: function does not accept missing data in covariates.")
	logit	= function(theta, a, b){
		return(log((theta-a)/(b-theta)))
	}


	logitInv	= function(z, a, b){
	  return ( b-(b-a)/(1+exp(z)) );
	}

	###########################
	##### Format the Data #####
	###########################

	index <- seq(from = (nburn+1), to = nsamp, by = thin)
	n 	<- dim(data)[1]
	q	<- 2

	names  <- colnames(model.matrix(formula, data))
	data$a <- data[,trt]
	data$y <- data[,all.vars(formula)[1]]
	data$ytemp 	<- 1
	nterm 	<- length(labels(terms(formula)))
	terms 	<- NULL
	for(ii in 1:nterm){
		if(ii == 1) terms <- labels(terms(formula))[ii]
		if(ii > 1) terms 	<- paste(terms, "+", labels(terms(formula))[ii], sep=" ")
	}
	formulatemp <- as.formula(paste("ytemp ~ ", terms))
	covs 	 	<- model.matrix(formulatemp, data)

	makeXmat	= function(Xvals){
		#Xvals	= cbind(rep(1,dim(Xvals)[[1]]), Xvals)
		p	= dim(Xvals)[[2]]
		Xout	= matrix(0, n*q, p*q)
		Xout[seq(1,n*q,q),1:p]		= Xvals
		Xout[seq(2,n*q,q),(p+1):(2*p)]= Xvals
		return(Xout)
	}

	X	= makeXmat(covs)
	p	= ncol(X)

	y0			= rep(NA,n)
	y1			= rep(NA,n)
	y0[data$a==0]	= data$y[data$a==0]
	y1[data$a==1]	= data$y[data$a==1]

	Y			= rep(-999,n*q)
	Y[seq(1,n*q,q)]	= y0
	Y[seq(2,n*q,q)]	= y1
	ismissy		= (is.na(Y))
	nwithmissing	= sum(ismissy)

	a			= rep(NA, n*q)
	a[seq(1,n*q,q)]	= data$a
	a[seq(2,n*q,q)]	= data$a

	

	###########################
	##### Select Priors #######
	###########################
	
	####Priors for AA' (Inverse Wishart)
	#####With q=2, this is just two inverse gamma priors
	KIG_a	= prior$KIG
	KIG_b	= prior$KIG
	
	####Priors for Psi (Inverse gamma)
	psiig_a	= prior$psi
	psiig_b	= prior$psi
	
	####Priors for theta (uniform)
	thetaunif_a	= prior$theta1 #miles
	thetaunif_b	= prior$theta2 #miles
	
	##################################
	##### Select Starting Values #####
	##################################

	my0	= summary(lm(y0~data$ps))
	my1	= summary(lm(y1~data$ps))

	missy1	= ismissy[seq(1,n*q,q)]
	missy2	= ismissy[seq(2,n*q,q)]
	missingindy	= list(missy1, missy2)
	
	Y[seq(1,n*q,q)][missy1==1]	= rnorm(sum(missy1==1), mean(Y[seq(1,n*q,q)], na.rm=T), 
							   sd(Y[seq(1,n*q,q)], na.rm=T))
	Y[seq(2,n*q,q)][missy2==1]	= rnorm(sum(missy2==1), mean(Y[seq(2,n*q,q)], na.rm=T), 
							   sd(Y[seq(2,n*1,q)], na.rm=T))	

	#Regression Coefficients
	if(is.null(starting$B) == F){
		B	= starting$B
	}
	if(is.null(starting$B) == T){
		B 	= rep(0, ncov*2)
	}

	#Spatial variance parameters, log transform for proposal
	if(is.null(starting$A) == T){
		initA1	= log(my0$sigma^2)
		initA2	= log(my1$sigma^2)
	}else{
		initA1	= starting$A
		initA2 	= starting$A
	}

	#Vector of log of residual variances (Psi)
	if(is.null(starting$psi) == T){
		initpsis	= log(c(my0$sigma^2, my1$sigma^2)*.1)
	}else{
		initpsis 	= log(rep(starting$psi, 2))
	}

	#Spatial Range parameters
	if(is.null(starting$theta) == T){
		inittheta	= c(1,1) # original scale
		inittheta	= logit(inittheta,thetaunif_a, thetaunif_b) #logit scale for proposals
	}else{
		inittheta 	= logit(starting$theta,thetaunif_a, thetaunif_b)
	}

	##################################
	##### Select Tuning Parameters ###
	##################################

	####Proposal SDS for A1 and A2
	A1propsds	= tuning$A
	A2propsds	= tuning$A
	
	####Proposal SDS for log(diag(Psi))
	psipropsds	= rep(tuning$psi, 2)
	
	###Proposal SDS for logit(theta) (aka, phi)
	thetapropsds	= tuning$theta


	#################################
	##### Define Some Functions #####
	#################################


	loglike_sp	= function(paramvals,Yvals){
		llike	= 0
		K1	= exp(paramvals[Aindex[1]])
		K2	= exp(paramvals[Aindex[2]])
			
		K	= createspatialsig(K1,K2,rho)
		
		if (!is.na(K)[[1]]){
			### Untransform theta to the original scale
			thetatemp	= logitInv(paramvals[thetaindex], thetaunif_a, thetaunif_b)
		
			det	= mvCovInvLogDet(coords=coords, cov.model="exponential",
					V=K, Psi=diag(exp(paramvals[psiindex]), nrow=q), 
					theta=thetatemp, modified.pp=FALSE, SWM=TRUE)
	
			## q inverse gamma priors for spatial variance
			###Jacobian for log transformation: \sum log(sigmasq)
			llike	= llike+ sum(-(KIG_a+1)*paramvals[Aindex] -KIG_b/exp(paramvals[Aindex]) + paramvals[Aindex])
		
			## q inverse gamma priors
			###Jacobian for log transformation: \sum log(sigmasq)
			llike	= llike+ sum(-(psiig_a+1)*paramvals[psiindex] -psiig_b/exp(paramvals[psiindex]) + paramvals[psiindex])
	
			## Uniform part for theta- this is the jacobian for the LogitInv transformation = (theta-a)(b-theta) / (b-a) 
			llike	= llike+ sum(log(thetatemp - thetaunif_a) + log(thetaunif_b - thetatemp))
	
			outlist		= list(llike, det$C, det$C.inv, det$log.det)
			names(outlist)	= c("ll","C","Cinv", "logdet")
		}# !isna(K)
	
		if (is.na(K)[[1]]){
			outlist		= list(-Inf, 1)
			names(outlist)	= c("ll", "notpd")
			}
	
		return(outlist)
	}	

	loglike_norm	= function(Yvals,Bvals,Cinvval,logdetval){
		##Normal Part
		YXB	= (Yvals-X%*%Bvals)
		llike	= -.5*logdetval -.5*t(YXB)%*%Cinvval%*%YXB
		return(llike)
	}

	
	MHstep_sp	= function(index, paramvals, Bvals, Yvals, Cval, Cinvval, logdetval,currentllsp, currentllnorm){
		accept	= 0
		notpd		= 1
		llretsp	= currentllsp
		llretnorm	= currentllnorm
		currentll	= currentllsp+currentllnorm
		Cret		= Cval
		Cinvret	= Cinvval
		logdetret	= logdetval
		currentparams	= paramvals
		props			= currentparams
		props[index]	= rnorm(length(index), paramvals[index], propsds[index])
		llanddet		= loglike_sp(props,Yvals)
		Cprop		= llanddet$C
		Cinvprop	= llanddet$Cinv
		logdetprop	= llanddet$logdet
		llpropsp	= llanddet$ll
	
		if (llanddet$ll != -Inf){
			notpd	= 0
			llpropnorm	= loglike_norm(Yvals,Bvals,Cinvprop,logdetprop)
			llprop	= llpropsp+llpropnorm

			ratio		= exp(llprop-currentll)
			ratio[ratio>1] = 1
			if(is.na(ratio) == T | ratio < 0) ratio <- -1	# LH: keep getting MH error and trying to bypass

			if (runif(1)<=ratio){
				currentparams[index]	= props[index]
				llretsp	= llpropsp
				llretnorm	= llpropnorm
				Cret		= Cprop
				Cinvret	= Cinvprop
				logdetret	= logdetprop
				accept	= 1
			}
		}## llanddet$ll !=-Inf
	
		outlist		= list(currentparams, llretsp,llretnorm, accept, Cret, Cinvret, logdetret, notpd)
		names(outlist) 	= c("params", "llsp","llnorm", "accepted", "C", "Cinv", "logdet", "notpd")
		return(outlist)
	}
	
	MHstep_sp_same	= function(index, paramvals, Bvals, Yvals, Cval, Cinvval, logdetval,currentllsp, currentllnorm){
		accept	= 0
		notpd		= 1
		llretsp	= currentllsp
		llretnorm	= currentllnorm
		currentll	= currentllsp+currentllnorm
		Cret		= Cval
		Cinvret	= Cinvval
		logdetret	= logdetval
		currentparams	= paramvals
		props			= currentparams
		props[index]	= rnorm(1, paramvals[index][1], propsds[index][1])
		llanddet		= loglike_sp(props,Yvals)
		Cprop		= llanddet$C
		Cinvprop	= llanddet$Cinv
		logdetprop	= llanddet$logdet
		llpropsp	= llanddet$ll
	
		if (llanddet$ll != -Inf){
			notpd	= 0
			llpropnorm	= loglike_norm(Yvals,Bvals,Cinvprop,logdetprop)
			llprop	= llpropsp+llpropnorm

			ratio		= exp(llprop-currentll)
			ratio[ratio>1] = 1
			if(is.na(ratio) == T | ratio < 0) ratio <- -1	# LH: keep getting MH error and trying to bypass

			if (runif(1)<=ratio){
				currentparams[index]	= props[index]
				llretsp	= llpropsp
				llretnorm	= llpropnorm
				Cret		= Cprop
				Cinvret	= Cinvprop
				logdetret	= logdetprop
				accept	= 1
			}
		}## llanddet$ll !=-Inf
	
		outlist		= list(currentparams, llretsp,llretnorm, accept, Cret, Cinvret, logdetret, notpd)
		names(outlist) 	= c("params", "llsp","llnorm", "accepted", "C", "Cinv", "logdet", "notpd")
		return(outlist)
	}
	


	updateBeta	= function(Cinvval, Yvals){
		S_beta	= solve(t(X)%*%Cinvval%*%X)
		Mu_beta  	= S_beta%*%t(X)%*%Cinvval%*%Yvals
		return(mvrnorm(1,Mu_beta,S_beta))
	}

	createspatialsig	= function(K1mat, K2mat, rho){
		sig		= matrix(0,q,q)
		sig[1,1]	= K1mat
		sig[2,2]	= K2mat
		sig[1,2]	= rho*sqrt(K1mat*K2mat)
		sig[2,1]	= sig[1,2]
	
		if(!is.positive.definite(sig)){
			sig	= NA
		}
		return(sig)
	}

	
	########################################
	##### Initialize Sampling Matrices #####
	########################################

	params	= c(initA1,initA2, initpsis, inittheta)  #Everything here is on the transformed scale for proposals
	nparams	= length(params)
	propsds	= c(A1propsds, A2propsds, psipropsds, thetapropsds)
	Aindex	= 1:2
	psiindex	= 3:4
	thetaindex	= 5
	accepted 	= rep(0, length(propsds))

	binsize 	= 1000
	rho		= 0
	notpd		= 0

	samples	= matrix(NA, nrow=length(index), ncol=nparams+1+length(B))
	dimnames(samples)[[2]]	= c("K[1,1]", "K[2,1]", "K[2,2]", paste("Psi", 1:q, sep=""), "Theta", 
					     paste("B0", 0:((p/2)-1), sep=""), paste("B1", 0:((p/2)-1), sep=""))
	ysims		= matrix(NA, nrow=length(index), ncol=length(Y))

	##Calculate initial log likelihood	
	initsp	= loglike_sp(params,Y)
	Cinv		= initsp$Cinv
	C		= initsp$C
	logdet 	= initsp$logdet
	llsp		= initsp$ll
	llnorm	= loglike_norm(Y,B,Cinv,logdet) 
	ll		= llsp+llnorm

	iterno	= 1
	donesampling= FALSE
	kk 		= 1
	
	while (donesampling==FALSE){
		B 	= updateBeta(Cinv,Y)
		llnorm= loglike_norm(Y,B,Cinv,logdet)
		ll	= llsp+llnorm
	
		######## PROPOSE #########
		###### Metropolis steps for spatial parameters #########
		#a0 parameters
		paramindex	= c(Aindex[1], psiindex[1])
		mhstep	= MHstep_sp(paramindex,params,B,Y, C, Cinv, logdet, llsp, llnorm)
		params	= mhstep$params
		llsp		= mhstep$llsp
		llnorm	= mhstep$llnorm
		Cinv		= mhstep$Cinv
		C		= mhstep$C
		logdet	= mhstep$logdet
		accepted[paramindex] = accepted[paramindex]+mhstep$accepted
		notpd		= notpd+mhstep$notpd
	
		#a1parameters
		paramindex	= c(Aindex[2], psiindex[2])
		mhstep	= MHstep_sp(paramindex,params,B,Y, C, Cinv, logdet, llsp, llnorm)
		params	= mhstep$params
		llsp		= mhstep$llsp
		llnorm	= mhstep$llnorm
		Cinv		= mhstep$Cinv
		C		= mhstep$C
		logdet	= mhstep$logdet
		accepted[paramindex]	= accepted[paramindex]+mhstep$accepted
		notpd		= notpd+mhstep$notpd
		
		#Spatial Range Parameter (fixed to be the same in both groups)
		paramindex	= c(thetaindex)
		mhstep	= MHstep_sp_same(paramindex,params,B,Y, C, Cinv, logdet, llsp, llnorm)
		params	= mhstep$params
		llsp		= mhstep$llsp
		llnorm	= mhstep$llnorm
		Cinv		= mhstep$Cinv
		C		= mhstep$C
		logdet	= mhstep$logdet
		accepted[paramindex]	= accepted[paramindex]+mhstep$accepted
		notpd		= notpd+mhstep$notpd
	
		ll 		= llsp+llnorm	
	
		#Simulate Missing Y
		Sy_ystar	= C[!ismissy, ismissy]
		Sy_y		= C[!ismissy,!ismissy]
		Sy_y_inv	= solve(Sy_y)
		Systar_ystar= C[ismissy,ismissy]
		
		m_pred	= X[ismissy,]%*%B + t(Sy_ystar)%*%Sy_y_inv%*%(Y[!ismissy] - X[!ismissy,]%*%B)
		S_pred	= Systar_ystar - t(Sy_ystar)%*%Sy_y_inv%*%Sy_ystar
			
		Y[ismissy]	= mvrnorm(1, m_pred,S_pred)
		
		llnorm= loglike_norm(Y,B,Cinv,logdet)  #### LOOK HERE LAUREN!!!!!!
	
		### Create output variables on the original scale
		
		K1out	= exp(params[Aindex[1]])
		K2out	= exp(params[Aindex[2]])
		Kout	= createspatialsig(K1out, K2out,rho)
	
		thetaout		= logitInv(params[thetaindex], thetaunif_a, thetaunif_b)
		
		if(iterno %in% index){
			samples[kk,]	= c(Kout[lower.tri(diag(1,q), TRUE)], exp(params[psiindex]), thetaout, B)
			ysims[kk,]		= Y
			kk <- kk + 1
		}
	
		if (iterno%%binsize==0){
			print(paste("Iteration:", iterno))
			print(c("Accepted:", round(accepted/iterno,3)))
			print(c("Number of not PD:", notpd))
		}
	
		if (iterno>=nsamp){donesampling=TRUE}	
	
		iterno = iterno+1

	}#while donesampling==FALSE
		
	out		<- list()
	out$samples <- samples
	out$y0	<- ysims[,seq(1,n*q,q)]
	out$y1 	<- ysims[,seq(2,n*q,q)]
	out$coords 	<- coords
	out$trt	<- data$a
	return(out)
}